R Programming Training by Experts

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Our Training Process

R Programming - Syllabus, Fees & Duration

THE ART OF R PROGRAMMING

    INTRODUCTION
    • Why Use R for Your Statistical Work?
    • Object-Oriented Programming
    • Functional Programming?
    • Functional Programming?
    INSTALLING R
    • Downloading R from CRAN
    • Installing from Source
    GETTING STARTED
      How to Run R
      • Interactive Mode
      • Batch Mode
    First R Session
      Introduction to Functions
      • Variable Scope
      • Default Arguments
      Preview of Some Important R Data Structures
      • Vectors, the R
      • Character Strings
      • Matrices
      • Lists
      • Arrays
      • Data Frames
      VECTORS
        Scalars, Vectors, Arrays, and Matrices
        • Adding and Deleting Vector Elements
        • Obtaining the Length of a Vector
        • Matrices and Arrays as Vectors
        Declarations
        Common Vector Operations
        • Vector Arithmetic and Logical Operations
        • Vector Indexing
        • Generating Useful Vectors with the : Operator
        • Generating Vector Sequences with seq()
        • Repeating Vector Constants with rep
        Vectorized Operations
        • Vector In, Vector Out
        • Vector In, Matrix Out
        NA and NULL Values
        • Using NA
        • Using NULL
        Filtering
        • Generating Filtering Indices
        • Filtering with the subset() Function
        • The Selection Function which
        A Vectorized if-then-else: The ifelse() Function
        • Extended Example: A Measure of Association
        • Extended Example: Recoding an Abalone Data Set
        Testing Vector Equality
        Vector Element Names
        More on c()
      MATRICES AND ARRAYS
        Creating Matrices
        • General Matrix Operations
        • Performing Linear Algebra Operations on Matrices
        • Matrix Indexing
        • Filtering on Matrices
        Applying Functions to Matrix Rows and Columns
        • Using the apply() Function
        • Extended Example: Finding Outliers
        • Adding and Deleting Matrix Rows and Columns
        • Changing the Size of a Matrix
        More on the Vector/Matrix Distinction
        Avoiding Unintended Dimension Reduction
        Naming Matrix Rows and Columns
        Higher-Dimensional Arrays
      LISTS
        Creating Lists
        General List Operations
        • List Indexing
        • Adding and Deleting List Elements
        • Getting the Size of a List
        Accessing List Components and Values
        Applying Functions to Lists
        • Using the lapply() and sapply() Functions
      ARRAYS
      • Naming Columns and Rows
      • Accessing Array Elements
      • Check if an Item Exists
      • Amount of Rows and Columns
      • Array Length
      • Manipulating Array Elements
      • Calculations Across Array Elements
      DATA FRAMES
        Creating Data Frames
        • Accessing Data Frames
        Other Matrix-Like Operations
        • Extracting Subdata Frames
        • More on Treatment of NA Values
        • Using the rbind() and cbind() Functions and Alternatives .
        • Applying apply()
        Merging Data Frames
        • Extended Example: An Employee Database
        Applying Functions to Data Frames
        • Using lapply() and sapply() on Data Frames
      FACTORS AND TABLES
        Factors and Levels
        Common Functions Used with Factors
        • The tapply() Function
        • The split() Function
        • The by() Function
        Working with Tables
        • Matrix/Array-Like Operations on Tables
        • Extended Example: Extracting a
        Other Factor- and Table-Related Functions
        • The aggregate() Function
        • The cut() Function
      R PROGRAMMING STRUCTURES
        Control Statements
        • Loops
        • Looping Over Non vector Sets
        • if-else
        Arithmetic and Boolean Operators and Values
        Default Values for Arguments
        Return Values
        • Deciding Whether to Explicitly Call return()
        • Returning Complex Objects
        Functions Are Objects
        Environment and Scope Issues
        The Top-Level Environment
        • The Scope Hierarchy
        • More on ls()
        • Functions Have (Almost) No Side Effects
        No Pointers in R
        Writing Upstairs
        • Writing to Nonlocals with the Super assignment Operator
        • Writing to Nonlocals with assign()
        When Should You Use Global Variables?
        Replacement Functions
        • What’s Considered a Replacement Function?
        Tools for Composing Function Code
        • Text Editors and Integrated Development Environments
        The edit() Function
        Writing Your Own Binary Operations
        Anonymous Functions
      DOING MATH AND SIMULATIONS IN R
        Math Functions
        • Extended Example
        • Cumulative Sums and Products
        • Minima and Maxima
        Functions for Statistical Distributions
        Sorting
        Linear Algebra Operations on Vectors and Matrices
        • Extended Example: Vector Cross Product
        • Set Operations
        Simulation Programming in R
        • Built-In Random Variate Generators
        • Obtaining the Same Random Stream in Repeated Runs
      INPUT/OUTPUT
        Accessing the Keyboard and Monitor
        • Using the scan() Function
        • Using the readline() Function
        • Printing to the Screen
        Reading and Writing Files
        • Reading a Data Frame or Matrix from a File
        • Reading Text Files
        • Introduction to Connections
        • Extended Example
        • Accessing Files on Remote Machines via URLs
        • Writing to a File
        • Getting File and Directory Information
      STRING MANIPULATION
        An Overview of String-Manipulation Functions
        • grep()
        • nchar()
        • paste()
        • sprintf()
        • substr
        • strsplit()
        • regexpr()
        Regular Expressions
        • Extended Example
      R DATA INTERFACES
        R - CSV Files
        • Reading a CSV File
        • Analyzing the CSV File
        • Writing into a CSV File
        R - Excel Files
        • Install xlsx Package
        • Reading the Excel File
        R - Binary Files
        • Writing the Binary File
        • Reading the Binary File
        R - XML Files
        • Reading XML File
        • XML to Data Frame
        R - JSON Files
        • Install rjson Package
        • Read the JSON File
        • Convert JSON to a Data Frame
        R - Database
        • RMySQL Package
        • Connecting R to MySql
        • Querying the Tables
        • Query with Filter Clause
        • Updating Rows in the Tables
        • Inserting Data into the Tables
        • Creating Tables in MySql
        • Dropping Tables in MySql
      GRAPHICS
        Creating Graphs
        • The Workhorse of R Base Graphics: The plot() Function
        • R - Pie Charts
        • R - Bar Charts
        • R - Boxplots
        • R - Histograms
        • R - Line Graphs
        • R - Scatterplots
        • Starting a New Graph While Keeping the Old Ones
        • Extended Example
        • Adding Points: The points() Function
        • Adding a Legend: The legend() Function
        • Adding Text: The text() Function
        • Pinpointing Locations: The locator() Function
        • Restoring a Plot
        • Customizing Graphs
        • Changing Character Sizes: The cex
        • Changing the Range of Axes: The xlim and ylim Options
        • Graphing Explicit Functions
        • Extended Example
        Saving Graphs to Files
        • R Graphics Devices
        • Saving the Displayed Graph
        • Closing an R Graphics Device
        Creating Three-Dimensional Plots
      R Statistics
        R Statistics Intro
        R Data Set
        R Max and Min
        R Mean Median Mode
        R Percentiles
      INSTALLING AND USING PACKAGES
        Package Basics
        Loading a Package from Your Hard Drive
        Downloading a Package from the Web
        Installing Packages Automatically
        Installing Packages Manually
        Listing the Functions in a Package

    Download Syllabus - R Programming
    This syllabus is not final and can be customized as per needs/updates
 
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R Programming Jobs in Tulsa

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R Programming Internship/Course Details

R Programming internship jobs in Tulsa
R Programming With the help of R programming, massive datasets may be analysed in less time. . Students and working professionals can enrol in our top online R Programming training and learn from industry experts who have extensive experience in R Programming advising and R Programming training in Kerala. There is a significant shortage of experts with R programming skills on the market, which brings attention to pursue. Nestsoft offer the best R programming training, starting with the fundamentals and advancing to complex analytics concepts. Many large companies, including prominent banks, IT, retail, healthcare, pharmaceutical, supply chain, and logistics industries, adopt R. In R, there are a variety of great packages that can aid in a fast data analysis. It is a simple programming language than, other programming languages, would have no requirements. While teaching R Programming in the classroom, our Nestsoft trainers discuss their previous and current project experiences with candidates, allowing them to gain exposure to real-world business experience. You'll learn how to build and setup software for a statistical programming environment, as well as how to represent generic programming language concepts in a high-level statistical language.

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